What are the best offline AI models for different use cases in 2025?

Model selection for offline deployment depends on hardware constraints, task requirements, and quality needs. General-purpose chat: Llama 3.1 8B (best quality at 8B scale, good instruction following), Mistral 7B (efficient, strong reasoning), Phi-3 Mini (3.8B parameters, surprisingly capable for size), and Qwen 2.5 7B (strong multilingual support). Code generation: CodeLlama 7B/13B (specialized for code completion and generation), DeepSeek Coder V2 Lite (16B, excellent code reasoning), and StarCoder2 (multiple sizes, trained on The Stack v2). Summarization and analysis: Llama 3.1 8B Instruct (strong comprehension), SOLAR 10.7B (efficient for document analysis), and Mixtral 8x7B (MoE architecture, only activates 12B per forward pass despite 46B total). Embedding and retrieval: nomic-embed-text (768-dim, excellent quality/size ratio), all-MiniLM-L6-v2 (384-dim, very fast), and BGE-M3 (multilingual, multi-granularity). Vision: LLaVA 1.6 7B (image understanding and description), Moondream 2 (1.9B, efficient for edge vision), and Florence-2 (versatile vision tasks). Hardware mapping: 8GB RAM systems run 7B Q4 models comfortably, 16GB systems handle 13B Q4 or 7B Q8, 32GB systems run 30B+ Q4 models, and Apple M-series with unified memory excels at larger models due to high memory bandwidth.